Kaen's Projects
State of the art Deep Neural Network to detect crack detection in buildings.
It detect crack in building using Deep learning and computer vision.
这是一个使用Python和PyQt5开发的一个计算机视觉辅助裂缝标注工具,标注工具先用边缘检测和形态学方法预识别裂缝,然后人工对结果进行标涂或擦除。除了此方法,工具还有其他多种方法,详情请见readme.md的介绍
Image classification Deep Learning model to detect cracks in buildings
Config files for my GitHub profile.
A deep reinforcement learning (DRL) based approach for spatial layout of land use and roads in urban communities. (Nature Computational Science)
Neural Network for recognizing parameters of earth cracks by their photos
动手学数据分析以项目为主线,知识点孕育其中,通过边学、边做、边引导来得到更好的学习效果
finding cracks in highway using some pattern recognition and machine learning methods.
程序员在家做饭方法指南。Programmer's guide about how to cook at home (Chinese).
Lightweight version of MAPPO to help you quickly migrate to your local environment.
LSTM network python
Building a neural network using Crack detection dataset created by SDNET2018
《机器学习》(西瓜书)公式推导解析,在线阅读地址:https://datawhalechina.github.io/pumpkin-book
Main objective is to create some script to carry out geophysical inversion under Python. Working on 1st step - create the main framework.
All Algorithms implemented in Python
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
OpenType Unicode fonts for Scientific, Technical, and Mathematical texts
主要存储Datawhale组队学习中“编程、数据结构与算法”方向的资料。
The first competitive instance segmentation approach that runs on small edge devices at real-time speeds.